


'''simple docstring'''



import math
import unittest

from transformers import BioGptConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
	import torch

	from transformers import (
	    BioGptForCausalLM,
	    BioGptForSequenceClassification,
	    BioGptForTokenClassification,
	    BioGptModel,
	    BioGptTokenizer,
	)
	from transformers.models.biogpt.modeling_biogpt import BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST





class 				__UpperCamelCase							:




	def __init__(  self		,							__a		,							__a=13		,							__a=7		,							__a=True		,							__a=True		,							__a=False		,							__a=True		,							__a=99		,							__a=32		,							__a=5		,							__a=4		,							__a=37		,							__a="gelu"		,							__a=0.1		,							__a=0.1		,							__a=512		,							__a=16		,							__a=2		,							__a=0.02		,							__a=3		,							__a=4		,							__a=None		,							):

						'''simple docstring'''





						__a     :			Any          =    parent
						__a     :			Dict          =    batch_size
						__a     :			int          =    seq_length
						__a     :			Optional[Any]          =    is_training
						__a     :			List[Any]          =    use_input_mask
						__a     :			int          =    use_token_type_ids
						__a     :			Dict          =    use_labels
						__a     :			Union[str, Any]          =    vocab_size
						__a     :			Optional[Any]          =    hidden_size
						__a     :			Optional[int]          =    num_hidden_layers
						__a     :			Tuple          =    num_attention_heads
						__a     :			List[Any]          =    intermediate_size
						__a     :			Optional[int]          =    hidden_act
						__a     :			Union[str, Any]          =    hidden_dropout_prob
						__a     :			Any          =    attention_probs_dropout_prob
						__a     :			str          =    max_position_embeddings
						__a     :			Any          =    type_vocab_size
						__a     :			Union[str, Any]          =    type_sequence_label_size
						__a     :			Union[str, Any]          =    initializer_range
						__a     :			int          =    num_labels
						__a     :			str          =    num_choices
						__a     :			List[Any]          =    scope




	def  __UpperCAmelCase				(  self				):

						'''simple docstring'''





						__a     :			Tuple          =    ids_tensor([self.batch_size, self.seq_length]		,							self.vocab_size				)

						__a     :			int          =    None
						if self.use_input_mask:
											__a     :			Tuple          =    random_attention_mask([self.batch_size, self.seq_length]				)

						__a     :			Optional[int]          =    None
						if self.use_token_type_ids:
											__a     :			List[str]          =    ids_tensor([self.batch_size, self.seq_length]		,							self.type_vocab_size				)

						__a     :			Optional[int]          =    None
						__a     :			Union[str, Any]          =    None
						__a     :			Tuple          =    None
						if self.use_labels:
											__a     :			int          =    ids_tensor([self.batch_size]		,							self.type_sequence_label_size				)
											__a     :			Union[str, Any]          =    ids_tensor([self.batch_size, self.seq_length]		,							self.num_labels				)
											__a     :			List[Any]          =    ids_tensor([self.batch_size]		,							self.num_choices				)

						__a     :			List[str]          =    self.get_config()

						return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels




	def  __UpperCAmelCase				(  self				):

						'''simple docstring'''





						return BioGptConfig(
						    vocab_size=self.vocab_size		,							hidden_size=self.hidden_size		,							num_hidden_layers=self.num_hidden_layers		,							num_attention_heads=self.num_attention_heads		,							intermediate_size=self.intermediate_size		,							hidden_act=self.hidden_act		,							hidden_dropout_prob=self.hidden_dropout_prob		,							attention_probs_dropout_prob=self.attention_probs_dropout_prob		,							max_position_embeddings=self.max_position_embeddings		,							type_vocab_size=self.type_vocab_size		,							is_decoder=lowerCAmelCase__		,							initializer_range=self.initializer_range		,							)




	def  __UpperCAmelCase				(  self		,							__a		,							__a		,							__a		,							__a		,							__a		,							__a		,							__a				):

						'''simple docstring'''





						__a     :			List[Any]          =    BioGptModel(config=lowerCAmelCase__				)
						model.to(lowerCAmelCase__				)
						model.eval()
						__a     :			str          =    model(lowerCAmelCase__		,							attention_mask=lowerCAmelCase__				)
						__a     :			Optional[int]          =    model(lowerCAmelCase__				)
						self.parent.assertEqual(result.last_hidden_state.shape		,							(self.batch_size, self.seq_length, self.hidden_size)				)




	def  __UpperCAmelCase				(  self		,							__a		,							__a		,							__a		,							__a		,							__a		,							__a		,							__a		,							__a		,							__a		,							):

						'''simple docstring'''





						__a     :			str          =    BioGptForCausalLM(config=lowerCAmelCase__				)
						model.to(lowerCAmelCase__				)
						model.eval()
						__a     :			str          =    model(lowerCAmelCase__		,							attention_mask=lowerCAmelCase__		,							token_type_ids=lowerCAmelCase__		,							labels=lowerCAmelCase__				)
						self.parent.assertEqual(result.logits.shape		,							(self.batch_size, self.seq_length, self.vocab_size)				)




	def  __UpperCAmelCase				(  self		,							__a		,							__a		,							__a		,							__a		,							__a		,							*__a				):

						'''simple docstring'''





						__a     :			Optional[int]          =    BioGptModel(config=lowerCAmelCase__				)
						model.to(lowerCAmelCase__				)
						model.eval()

						# create attention mask
						__a     :			str          =    torch.ones(input_ids.shape		,							dtype=torch.long		,							device=lowerCAmelCase__				)
						__a     :			int          =    self.seq_length // 2
						__a     :			Union[str, Any]          =    0

						# first forward pass
						__a     :			int          =    model(lowerCAmelCase__		,							attention_mask=lowerCAmelCase__				).to_tuple()

						# create hypothetical next token and extent to next_input_ids
						__a     :			Dict          =    ids_tensor((self.batch_size, 1)		,							config.vocab_size				)

						# change a random masked slice from input_ids
						__a     :			Any          =    ids_tensor((1,)		,							lowerCAmelCase__				).item() + 1
						__a     :			int          =    ids_tensor((self.batch_size, 1)		,							config.vocab_size				).squeeze(-1				)
						__a     :			List[str]          =    random_other_next_tokens

						# append to next input_ids and attn_mask
						__a     :			Dict          =    torch.cat([input_ids, next_tokens]		,							dim=-1				)
						__a     :			Optional[Any]          =    torch.cat(
						    [attn_mask, torch.ones((attn_mask.shape[0], 1)		,							dtype=torch.long		,							device=lowerCAmelCase__				)]		,							dim=1		,							)

						# get two different outputs
						__a     :			Tuple          =    model(lowerCAmelCase__		,							attention_mask=lowerCAmelCase__				)['''last_hidden_state''']
						__a     :			Optional[Any]          =    model(lowerCAmelCase__		,							past_key_values=lowerCAmelCase__		,							attention_mask=lowerCAmelCase__				)['''last_hidden_state''']

						# select random slice
						__a     :			Union[str, Any]          =    ids_tensor((1,)		,							output_from_past.shape[-1]				).item()
						__a     :			str          =    output_from_no_past[:, -1, random_slice_idx].detach()
						__a     :			Dict          =    output_from_past[:, 0, random_slice_idx].detach()

						# test that outputs are equal for slice
						self.parent.assertTrue(torch.allclose(lowerCAmelCase__		,							lowerCAmelCase__		,							atol=1E-3				)				)




	def  __UpperCAmelCase				(  self		,							__a		,							__a		,							__a		,							__a		,							__a		,							*__a				):

						'''simple docstring'''





						__a     :			List[str]          =    BioGptModel(config=lowerCAmelCase__				).to(lowerCAmelCase__				).eval()

						__a     :			Optional[int]          =    torch.ones(input_ids.shape		,							dtype=torch.long		,							device=lowerCAmelCase__				)

						# first forward pass
						__a     :			Optional[Any]          =    model(lowerCAmelCase__		,							attention_mask=lowerCAmelCase__		,							use_cache=lowerCAmelCase__				)

						__a     :			List[str]          =    outputs.to_tuple()

						# create hypothetical multiple next token and extent to next_input_ids
						__a     :			List[str]          =    ids_tensor((self.batch_size, 3)		,							config.vocab_size				)
						__a     :			Optional[Any]          =    ids_tensor((self.batch_size, 3)		,							2				)

						# append to next input_ids and
						__a     :			Optional[int]          =    torch.cat([input_ids, next_tokens]		,							dim=-1				)
						__a     :			List[Any]          =    torch.cat([attention_mask, next_attn_mask]		,							dim=-1				)

						__a     :			Dict          =    model(lowerCAmelCase__		,							attention_mask=lowerCAmelCase__				)['''last_hidden_state''']
						__a     :			Optional[int]          =    model(lowerCAmelCase__		,							attention_mask=lowerCAmelCase__		,							past_key_values=lowerCAmelCase__				)[
						    '''last_hidden_state'''
						]

						# select random slice
						__a     :			Optional[Any]          =    ids_tensor((1,)		,							output_from_past.shape[-1]				).item()
						__a     :			List[str]          =    output_from_no_past[:, -3:, random_slice_idx].detach()
						__a     :			Optional[int]          =    output_from_past[:, :, random_slice_idx].detach()

						self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]				)

						# test that outputs are equal for slice
						self.parent.assertTrue(torch.allclose(lowerCAmelCase__		,							lowerCAmelCase__		,							atol=1E-3				)				)




	def  __UpperCAmelCase				(  self		,							__a		,							__a		,							__a		,							__a		,							__a		,							*__a		,							__a=False				):

						'''simple docstring'''





						__a     :			Union[str, Any]          =    BioGptForCausalLM(lowerCAmelCase__				)
						model.to(lowerCAmelCase__				)
						if gradient_checkpointing:
											model.gradient_checkpointing_enable()

						__a     :			int          =    model(lowerCAmelCase__		,							labels=lowerCAmelCase__				)
						self.parent.assertEqual(result.loss.shape		,							()				)
						self.parent.assertEqual(result.logits.shape		,							(self.batch_size, self.seq_length, self.vocab_size)				)
						result.loss.backward()




	def  __UpperCAmelCase				(  self		,							__a		,							*__a				):

						'''simple docstring'''





						__a     :			Any          =    BioGptModel(lowerCAmelCase__				)
						__a     :			str          =    model.config.initializer_range / math.sqrt(2 * model.config.num_hidden_layers				)
						for key in model.state_dict().keys():
											if "c_proj" in key and "weight" in key:
																self.parent.assertLessEqual(abs(torch.std(model.state_dict()[key]				) - model_std				)		,							0.001				)
																self.parent.assertLessEqual(abs(torch.mean(model.state_dict()[key]				) - 0.0				)		,							0.01				)




	def  __UpperCAmelCase				(  self		,							__a		,							__a		,							__a		,							__a		,							__a		,							*__a				):

						'''simple docstring'''





						__a     :			Optional[Any]          =    self.num_labels
						__a     :			Union[str, Any]          =    BioGptForTokenClassification(lowerCAmelCase__				)
						model.to(lowerCAmelCase__				)
						model.eval()
						__a     :			str          =    model(lowerCAmelCase__		,							attention_mask=lowerCAmelCase__		,							token_type_ids=lowerCAmelCase__				)
						self.parent.assertEqual(result.logits.shape		,							(self.batch_size, self.seq_length, self.num_labels)				)





	def  __UpperCAmelCase				(  self				):

						'''simple docstring'''





						__a     :			Optional[int]          =    self.prepare_config_and_inputs()
						(
						    __a
						)     :			Union[str, Any]          =    config_and_inputs
						__a     :			str          =    {'''input_ids''': input_ids, '''attention_mask''': input_mask}
						return config, inputs_dict





@require_torch
class 				__UpperCamelCase							(						snake_case_						, snake_case_						, snake_case_						, unittest.TestCase   ):
	A_           =       (
	    (BioGptModel, BioGptForCausalLM, BioGptForSequenceClassification, BioGptForTokenClassification)
	    if is_torch_available()
	    else ()
	)
	A_           =       (BioGptForCausalLM,) if is_torch_available() else ()
	A_           =       (
	    {
	        'feature-extraction': BioGptModel,
	        'text-classification': BioGptForSequenceClassification,
	        'text-generation': BioGptForCausalLM,
	        'token-classification': BioGptForTokenClassification,
	        'zero-shot': BioGptForSequenceClassification,
	    }
	    if is_torch_available()
	    else {}
	)
	A_           =       False




	def  __UpperCAmelCase				(  self				):

						'''simple docstring'''





						__a     :			List[Any]          =    BioGptModelTester(self				)
						__a     :			Any          =    ConfigTester(self		,							config_class=lowerCAmelCase__		,							hidden_size=37				)




	def  __UpperCAmelCase				(  self				):

						'''simple docstring'''





						self.config_tester.run_common_tests()




	def  __UpperCAmelCase				(  self				):

						'''simple docstring'''





						__a     :			Any          =    self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*lowerCAmelCase__				)




	def  __UpperCAmelCase				(  self				):

						'''simple docstring'''





						__a     :			Optional[Any]          =    self.model_tester.prepare_config_and_inputs()
						for type in ["absolute", "relative_key", "relative_key_query"]:
											__a     :			Optional[Any]          =    type
											self.model_tester.create_and_check_model(*lowerCAmelCase__				)




	def  __UpperCAmelCase				(  self				):

						'''simple docstring'''





						__a     :			Dict          =    self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_biogpt_model_attention_mask_past(*lowerCAmelCase__				)




	def  __UpperCAmelCase				(  self				):

						'''simple docstring'''





						__a     :			Dict          =    self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_forward_and_backwards(*lowerCAmelCase__		,							gradient_checkpointing=lowerCAmelCase__				)




	def  __UpperCAmelCase				(  self				):

						'''simple docstring'''





						__a     :			List[Any]          =    self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_biogpt_model_past_large_inputs(*lowerCAmelCase__				)




	def  __UpperCAmelCase				(  self				):

						'''simple docstring'''





						__a     :			Optional[int]          =    self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_biogpt_weight_initialization(*lowerCAmelCase__				)




	def  __UpperCAmelCase				(  self				):

						'''simple docstring'''





						__a     :			Union[str, Any]          =    self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_biogpt_for_token_classification(*lowerCAmelCase__				)




	@slow
	def  __UpperCAmelCase				(  self				):

						'''simple docstring'''





						__a     :			List[str]          =    BioGptForCausalLM.from_pretrained('microsoft/biogpt'				)
						model.to(lowerCAmelCase__				)
						__a     :			Union[str, Any]          =    BioGptTokenizer.from_pretrained('microsoft/biogpt'				)

						__a     :			List[str]          =    '''left'''

						# Define PAD Token = EOS Token = 50256
						__a     :			Tuple          =    tokenizer.eos_token
						__a     :			Optional[Any]          =    model.config.eos_token_id

						# use different length sentences to test batching
						__a     :			int          =    [
						    '''Hello, my dog is a little''',
						    '''Today, I''',
						]

						__a     :			Optional[Any]          =    tokenizer(lowerCAmelCase__		,							return_tensors='pt'		,							padding=lowerCAmelCase__				)
						__a     :			Optional[Any]          =    inputs['''input_ids'''].to(lowerCAmelCase__				)

						__a     :			Union[str, Any]          =    model.generate(
						    input_ids=lowerCAmelCase__		,							attention_mask=inputs['attention_mask'].to(lowerCAmelCase__				)		,							)

						__a     :			List[Any]          =    tokenizer(sentences[0]		,							return_tensors='pt'				).input_ids.to(lowerCAmelCase__				)
						__a     :			Optional[Any]          =    model.generate(input_ids=lowerCAmelCase__				)

						__a     :			Optional[int]          =    inputs_non_padded.shape[-1] - inputs['''attention_mask'''][-1].long().sum().cpu().item()
						__a     :			Tuple          =    tokenizer(sentences[1]		,							return_tensors='pt'				).input_ids.to(lowerCAmelCase__				)
						__a     :			Optional[int]          =    model.generate(input_ids=lowerCAmelCase__		,							max_length=model.config.max_length - num_paddings				)

						__a     :			List[Any]          =    tokenizer.batch_decode(lowerCAmelCase__		,							skip_special_tokens=lowerCAmelCase__				)
						__a     :			List[Any]          =    tokenizer.decode(output_non_padded[0]		,							skip_special_tokens=lowerCAmelCase__				)
						__a     :			List[str]          =    tokenizer.decode(output_padded[0]		,							skip_special_tokens=lowerCAmelCase__				)

						__a     :			Dict          =    [
						    '''Hello, my dog is a little bit bigger than a little bit.''',
						    '''Today, I have a good idea of how to use the information''',
						]
						self.assertListEqual(lowerCAmelCase__		,							lowerCAmelCase__				)
						self.assertListEqual(lowerCAmelCase__		,							[non_padded_sentence, padded_sentence]				)




	@slow
	def  __UpperCAmelCase				(  self				):

						'''simple docstring'''





						for model_name in BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											__a     :			Optional[Any]          =    BioGptModel.from_pretrained(lowerCAmelCase__				)
											self.assertIsNotNone(lowerCAmelCase__				)




	def  __UpperCAmelCase				(  self				):

						'''simple docstring'''





						__a     :			Optional[Any]          =    self.model_tester.prepare_config_and_inputs_for_common()
						__a     :			Optional[int]          =    3
						__a     :			Union[str, Any]          =    input_dict['''input_ids''']
						__a     :			List[str]          =    input_ids.ne(1				).to(lowerCAmelCase__				)
						__a     :			Tuple          =    ids_tensor([self.model_tester.batch_size]		,							self.model_tester.type_sequence_label_size				)
						__a     :			int          =    BioGptForSequenceClassification(lowerCAmelCase__				)
						model.to(lowerCAmelCase__				)
						model.eval()
						__a     :			str          =    model(lowerCAmelCase__		,							attention_mask=lowerCAmelCase__		,							labels=lowerCAmelCase__				)
						self.assertEqual(result.logits.shape		,							(self.model_tester.batch_size, self.model_tester.num_labels)				)





	def  __UpperCAmelCase				(  self				):

						'''simple docstring'''





						__a     :			Optional[Any]          =    self.model_tester.prepare_config_and_inputs_for_common()
						__a     :			List[Any]          =    3
						__a     :			Union[str, Any]          =    '''multi_label_classification'''
						__a     :			Dict          =    input_dict['''input_ids''']
						__a     :			Dict          =    input_ids.ne(1				).to(lowerCAmelCase__				)
						__a     :			Union[str, Any]          =    ids_tensor(
						    [self.model_tester.batch_size, config.num_labels]		,							self.model_tester.type_sequence_label_size				).to(torch.float				)
						__a     :			str          =    BioGptForSequenceClassification(lowerCAmelCase__				)
						model.to(lowerCAmelCase__				)
						model.eval()
						__a     :			Any          =    model(lowerCAmelCase__		,							attention_mask=lowerCAmelCase__		,							labels=lowerCAmelCase__				)
						self.assertEqual(result.logits.shape		,							(self.model_tester.batch_size, self.model_tester.num_labels)				)





@require_torch
class 				__UpperCamelCase							(						unittest.TestCase   ):




	@slow
	def  __UpperCAmelCase				(  self				):

						'''simple docstring'''





						__a     :			str          =    BioGptForCausalLM.from_pretrained('microsoft/biogpt'				)
						__a     :			str          =    torch.tensor([[2, 4805, 9, 656, 21]]				)
						__a     :			Any          =    model(lowerCAmelCase__				)[0]

						__a     :			Union[str, Any]          =    4_2384

						__a     :			Optional[Any]          =    torch.Size((1, 5, vocab_size)				)
						self.assertEqual(output.shape		,							lowerCAmelCase__				)

						__a     :			Tuple          =    torch.tensor(
						    [[[-9.5236, -9.8918, 10.4557], [-11.0469, -9.6423, 8.1022], [-8.8664, -7.8826, 5.5325]]]				)

						self.assertTrue(torch.allclose(output[:, :3, :3]		,							lowerCAmelCase__		,							atol=1E-4				)				)





	@slow
	def  __UpperCAmelCase				(  self				):

						'''simple docstring'''





						__a     :			List[str]          =    BioGptTokenizer.from_pretrained('microsoft/biogpt'				)
						__a     :			Tuple          =    BioGptForCausalLM.from_pretrained('microsoft/biogpt'				)
						model.to(lowerCAmelCase__				)

						torch.manual_seed(0				)
						__a     :			int          =    tokenizer('COVID-19 is'		,							return_tensors='pt'				).to(lowerCAmelCase__				)
						__a     :			List[Any]          =    model.generate(
						    **lowerCAmelCase__		,							min_length=100		,							max_length=1024		,							num_beams=5		,							early_stopping=lowerCAmelCase__		,							)
						__a     :			Dict          =    tokenizer.decode(output_ids[0]		,							skip_special_tokens=lowerCAmelCase__				)

						__a     :			List[str]          =    (
						    '''COVID-19 is a global pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the'''
						    ''' causative agent of coronavirus disease 2019 (COVID-19), which has spread to more than 200 countries and'''
						    ''' territories, including the United States (US), Canada, Australia, New Zealand, the United Kingdom (UK),'''
						    ''' and the United States of America (USA), as of March 11, 2020, with more than 800,000 confirmed cases and'''
						    ''' more than 800,000 deaths.'''
						)
						self.assertEqual(lowerCAmelCase__		,							lowerCAmelCase__				)








'''simple docstring'''



lowerCAmelCase_  :							List[Any]             =    [0, 2, 4, 6, 8]
lowerCAmelCase_  :							str             =    [1, 3, 5, 7, 9]
def        UpperCAmelCase (    A	:					int       ,		A	:					int       ,		A	:					list[int]       ,		A	:					int					):
							if remaining_length == 0:
														if digits[0] == 0 or digits[-1] == 0:
																					return 0

														for i in range(length // 2 - 1       ,		-1       ,		-1					):
																					remainder += digits[i] + digits[length - i - 1]

																					if remainder % 2 == 0:
																												return 0

																					remainder //= 10

														return 1

							if remaining_length == 1:
														if remainder % 2 == 0:
																					return 0

														SCREAMING_SNAKE_CASE       :   List[Any]         =      0
														for digit in range(10					):
																					SCREAMING_SNAKE_CASE       :   Optional[Any]         =      digit
																					result += reversible_numbers(
																					    0       ,		(remainder + 2 * digit) // 10       ,		A       ,		A					)
														return result

							SCREAMING_SNAKE_CASE       :   List[str]         =      0
							for digita in range(10					):
														SCREAMING_SNAKE_CASE       :   int         =      digita

														if (remainder + digita) % 2 == 0:
																					SCREAMING_SNAKE_CASE       :   str         =      ODD_DIGITS
														else:
																					SCREAMING_SNAKE_CASE       :   List[str]         =      EVEN_DIGITS

														for digita in other_parity_digits:
																					SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      digita
																					result += reversible_numbers(
																					    remaining_length - 2       ,		(remainder + digita + digita) // 10       ,		A       ,		A       ,		)
							return result
def        UpperCAmelCase (    A	:					int = 9					):
							SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      0
							for length in range(1       ,		max_power + 1					):
														result += reversible_numbers(A       ,		0       ,		[0] * length       ,		A					)
							return result


if __name__ == "__main__":
							print(f'{solution() = }')




